Although the direct and easy approach to solve the registration problem is to work with data from the same modality, i.e., image-to-image and point cloud-to-point cloud, several limitations exist in these same-modality registration approaches.
For point cloud-to-point cloud registration, it is impractical and costly to mount
expensive and hard-to-maintain Lidars on large fleet of robots and mobile devices during operations. Furthermore, feature-based point cloud-to-point cloud registration [1]}, [2]}, [3]}, [4]} usually requires storage of \(D\) -dimensional features (\(D\gg 3\) ) in addition to the \((x,y,z)\)  point coordinates, which increases the memory complexity. For image-to-image registration, meticulous effort is required to perform SfM [5]}, [6]}, [7]} and store the image feature descriptors [8]}, [9]} corresponding to the reconstructed 3D points for feature matching. Additionally, image features are subjected to illumination conditions, seasonal changes, etc. Consequently, the image features stored in the map acquired in one season/time are hopeless for registration after a change in the season/time.
